This website contains some of the results from TESS analysis using data of the Syntactic Atlas of Swiss German (SADS). These results provide supporting information for the main article Linguistic traits as heritable units? Spatial Bayesian clustering reveals Swiss German dialect regions.

Tess results with \(K = 6\)

These results have been obtained with the Admixture model of TESS using the least correlated syntactic phenomena of the Syntactic Atlas of Swiss German (SADS). Choosing 6 populations (\(K=6\)), we obtain the following results.

Loading libraries 

library(tmap)
library(sf)
library(pophelper)
library(dplyr)
library(RColorBrewer)

Reading data

Read combined results with CLUMPP and merge them 

#Read clumpp results
K6 <- readQ("../../Results/CLUMPP_output/pop_K6-combined-merged.txt")

#Merging results of 6 populations
merged_data <- mergeQ(K6)[[1]]

#Import coordinates
coord<-read.table('../coordinates_wgs84.txt',header = T)

#Spatialize results
k6_results<-cbind(coord,merged_data)

#Convert data to sf format
point_data<-st_as_sf(k6_results, coords = c("X_1", "Y_1"), crs = 4326)

#Read municipalities
municipalities <- st_read(
  "../../shapefiles/municipalities_voronoi.shp")
## Reading layer `municipalities_voronoi' from data source `/home/noe/Desktop/Paper/Romano_PdM/shapefiles/municipalities_voronoi.shp' using driver `ESRI Shapefile'
## Simple feature collection with 356 features and 0 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 7.025109 ymin: 45.91675 xmax: 10.09691 ymax: 47.80846
## CRS:            4326
#Create ID in municipalities
municipalities$id <- 1:nrow(municipalities)

Results

Population 1 

mypaletTESS<-c("#1F78B4","#33A02C","#E31A1C","#FF7F00","#6A3D9A",'deeppink1')



palette <-colorRampPalette(c("white", mypaletTESS[6]))

population_1_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster1','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster1 = mean(Cluster1, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_1_aggregated) +
tm_polygons("Cluster1", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
)

Population 2 

palette <-colorRampPalette(c("white", mypaletTESS[4]))

population_2_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster2','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster2 = mean(Cluster2, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_2_aggregated) +
tm_polygons("Cluster2", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
)

Population 3 

palette <-colorRampPalette(c("white", mypaletTESS[5]))

population_3_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster3','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster3 = mean(Cluster3, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_3_aggregated) +
tm_polygons("Cluster3", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
)

Population 4 

palette <-colorRampPalette(c("white", mypaletTESS[3]))

population_4_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster4','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster4 = mean(Cluster4, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_4_aggregated) +
tm_polygons("Cluster4", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)

Population 5 

palette <-colorRampPalette(c("white", mypaletTESS[2]))

population_5_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster5','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster5 = mean(Cluster5, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_5_aggregated) +
tm_polygons("Cluster5", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)

Population 6 

palette <-colorRampPalette(c("white", mypaletTESS[1]))

population_6_aggregated = municipalities %>%
  st_join(point_data[,c('Cluster6','geometry')]) %>%
  group_by(id) %>%
  summarize(Cluster6 = mean(Cluster6, na.rm = TRUE))

tmap_mode("view")
tm_shape(population_6_aggregated) +
tm_polygons("Cluster6", palette = palette(15), breaks = c(0, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
) +
   tm_layout(frame = F, main.title = "Admixture proportions",legend.outside = T)